Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 71-79.doi: 10.11896/jsjkx.230500218

• Granular Computing & Knowledge Discovery • Previous Articles     Next Articles

Optimal Granularity Selection and Attribute Reduction in Meso-granularity Space

LI Teng1, LI Deyu1,2, ZHAI Yanhui1,2, ZHANG Shaoxia3   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University),Ministry of Education,Taiyuan 030006,China
    3 College of Information,Shanxi University of Finance and Economics,Taiyuan 030006,China
  • Received:2023-05-29 Revised:2023-07-27 Online:2023-10-10 Published:2023-10-10
  • About author:LI Teng,born in 1999, master.His main research interests include data mining and formal concept analysis.LI Deyu,born in 1965, Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include data mining and multi-label learning.
  • Supported by:
    National Natural Science Foundation of China(62072294,61972238) and Fundamental Research Program of Shanxi Province(202103021223303).

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Abstract: The conventional formal concept analysis adopts a meso-granularity formal context to meet the requirements of cross-layer granulation of data.However,it does not effectively combine the search for optimal granularity with attribute reduction,nor does it efficiently solve the problem of combination explosion in a multi-granular context.Therefore,based on the connection between granularity selection and attribute reduction in the meso-granularity,a new optimal granularity selection method(i.e.,optimal granularity reduction) is proposed to synchronize the selection of the optimal granularity and attribute reduction.In view of the combination explosion in searching for optimal granularity reduction,a stepwise search method is designed to update the gra-nularity space with searched information,eliminating a large number of non-optimal granularity reduction and significantly improving search efficiency.Experimental results demonstrate the effectiveness and superiority of this method.

Key words: Formal concept analysis, Multi-granularity decision formal context, Optimal granularity, Attribute reduction, Granular computing

CLC Number: 

  • TP182
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